Over the years cardiologists and electro-cardiologists have developed a body of knowledge pertaining to the analysis of electrocardiogram signals or ECG's. They have identified a number of basic “shapes” that correspond to basic heart syndromes. As of last count, more than 80 basic syndromes can be clearly linked with particular morphologies of the ECG signal (ABC of Clinical Electrocardiography by Francis Morrus, BMJ Publishing Group, 01-2003, ISBN 0727915363) (ECG's by Example, by Jenkings and Gerred, 1997, ISBN 0443058978). These syndromes include ischemic heart disease, hypertrophy patterns, atrioventricular blocks, bundle branch blocks, supraventricular rhythms and ventricular rhythms.
Previous work in analyzing ECG's has focused on building specific detectors for known syndromes. Typically a cardiologist provides a detailed morphological description of what to look for in the signal and this knowledge is encoded in a series of rules that codify an algorithm. This rule-based approach to detection/classification of ECG signals and the potential syndromes they encode has many drawbacks. Among others, clearly this is a time consuming approach that involves a trial and error method of algorithmic design. In addition, the algorithm designer is not exposed to large amounts of data and there is no guarantee that the rules encoding the algorithm are generic enough. Also, extracting the rules from the expert is difficult; sometimes experts don't know exactly how to distinguish one cardiac syndrome from another, they just “know” and cannot explain why they can make the distinction.
Other approaches use ECG data with annotations provided by a cardiologist. The expert assigns labels to regions of the ECG signal indicating whether the signal is normal or if a particular syndrome is present. Then pattern recognition techniques extract features from the ECG signal and using the labels try to build classifiers that minimize the error rate. While this application is better than the previous one and in general does not depend on a detailed understanding of the morphology of the signal (it only requires a label), it fails to take advantage of the extensive knowledge that experts have acquired over the years. Applicants have found that, in effect, too much is demanded from the pattern recognition algorithm that has to extract meaningful features from raw data and figure out on its own the rules that codify a particular syndrome.